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Xu Bai

7 accepted papers

2026

Rethinking Efficient Graph Coarsening via a Non-Selfishness Principle

ICML 2026poster

Graph coarsening is a graph dimensionality reduction technique that aims to construct a smaller and more tractable graph while preserving the essential structural and semantic properties of the original graph. However, most existing methods rely on pair-wise similarity matching, where each node inde…

Cited by 0SourceScholar
2025

Dual-Path Counterfactual Integration for Multimodal Aspect-Based Sentiment Classification

EMNLP 2025

Multimodal aspect-based sentiment classification (MABSC) requires fine-grained reasoning over both textual and visual content to infer sentiments toward specific aspects. However, existing methods often rely on superficial correlations—particularly between aspect terms and sentiment labels—leading t

Cited by 0SourcePDFScholar
2025

Efficient Non-Sequential Relational Modeling for Temporal Knowledge Graph Link Predictions

ICASSP 2025accepted

Temporal Knowledge Graphs (TKGs) are being widely explored to predict the future for they record multi-relational knowledge and the happening time of real-life facts. Existing works learn sequential patterns to infer the future from past facts in TKGs for predictions. Although achieving promising re…

Cited by 0SourceScholar
2025

Improving Embeddings by Refining Meanings for Temporal Knowledge Graph Link Predictions

ICASSP 2025accepted

Temporal Knowledge Graphs (TKGs) represent real-life facts using entities, relational types, and timestamps where relational types state the semantic scenario of facts. Current methods learn embeddings by merging facts of multiple types (e.g. sport and family) for predictions. Such embeddings associ…

Cited by 0SourceScholar
2025

LayerNavigator: Finding Promising Intervention Layers for Efficient Activation Steering in Large Language Models

NeurIPS 2025poster

Activation steering is an efficient technique for aligning the behavior of large language models (LLMs) by injecting steering vectors directly into a model’s residual stream during inference. A pivotal challenge in this approach lies in choosing the right layers to intervene, as inappropriate select…

Cited by 0SourcecodeScholar
2024

Tendon Driven Bistable Origami Flexible Gripper for High-Speed Adaptive Grasping

RA-L 2024

This paper introduces a novel bistable origami flexible gripper, which is based on a single-vertex and multi-crease (SVMC) origami structure that has sxeveral advantages, including a simple structure, low cost, and strong deformation capacity. This design addresses the drawbacks of slow response spe

Cited by 39SourceScholar
2022

Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis

NAACL 2022long

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task. Most recent efforts adopt pre-trained model to classify the sentences with aspects. However, the aspect sentiment bias from pre-trained model brings some noise to the ABSA task. Besides, traditional methods using…

Cited by 21SourcePDFScholar